Heterogeneity and Complexity are the main reasons why carbonate reservoirs offer a great challenge for its characterization compared to siliciclastic reservoirs. Carbonate reservoirs are known for its variable pore type and this variability can affect the Vp value up to 40 %. Pore type can vary depending on its depositional environment and diagenetic processes and these pore types are highly correlated with permeability. Differential Effective Medium is used to model the elastic modulus of effective medium that takes into account the effect of complexity of rock pore type. This complexity, in modeling, is divided into three geophysical pore types, which are stiff pore, interparticle pore, and microcrack. The resulting rock physics model is then used to calculate the value of Vs. Pore type inversion shows that the dominant pore types in this study area are interparticle and microcrack. The results of 1D modeling are then distributed to seismic volume to map the spatial distribution of pore type. Sensitivity analysis shows that acoustic impedance, shear impedance, and porosity have a good correlation with pore type. Therefore, Probabilistic Neural Network is used to distribute 1D pore type to seismic volume by using acoustic impedance, shear impedance, and porosity as a training data. The resulting volume is then used to interpret the zones with best permeability © Published under licence by IOP Publishing Ltd.